Results 201 to 210 of about 8,116 (238)

UW-PINN: Ultra-Weak PINNs for the acoustic wavesystem

2023
Deep learning methods for solving partial differential equations (PDEs) haverecently gained importance thanks to PINNs (Physics Informed Neural Net-works) which can be used to approximate the solution to the problem byavoiding the often delicate and costly discretization step of standard meth-ods such as finite differences or finite elements via the ...
Barucq, Hélène, Calandra, Henri, Maron, Thibaut   +2 more
openaire   +1 more source

PiNNs

This repository contains data for reproducing analysis and results from the work on "Process-informed neural networks: a hybrid modelling approach to improve predictive performance and inference of neural networks in ecology and beyond".
Dormann, Carsten, Moser, Niklas, Wesselkamp, Marieke   +2 more
openaire   +1 more source

Interface PINNs (I-PINNs): A physics-informed neural networks framework for interface problems

Computer Methods in Applied Mechanics and Engineering
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Antareep Kumar Sarma, Sumanta Roy, Chandrasekhar Annavarapu, Pratanu Roy, Shriram Jagannathan   +4 more
openaire   +2 more sources

Generalization of PINNs for elliptic interface problems

Applied Mathematics Letters
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xuelian Jiang, Ziming Wang, Wei Bao, Yingxiang Xu   +3 more
openaire   +3 more sources

Convergence Analysis of PINNs with Over-Parameterization

Communications in Computational Physics
Summary: Recently, physics-informed neural networks (PINNs) have been shown to be a simple and efficient method for solving PDEs empirically. However, the numerical analysis of PINNs is still incomplete, especially why over-parameterized PINNs work remains unknown.
Chen, Mo, Ding, Zhao, Jiao, Yuling, Lu, Xiliang, Wu, Peiying, Yang, Jerry Zhijian   +5 more
openaire   +2 more sources

Current density impedance imaging with PINNs

Journal of Computational and Applied Mathematics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chenguang Duan, Junjun Huang, Yuling Jiao, Xiliang Lu, Jerry Zhijian Yang   +4 more
openaire   +3 more sources

An interview with Dr. Vivian Pinn

2018
Interviewee: Vivian Pinn, MD was the first full-time Director of the Office of Research on Women’s Health (ORWH) from 1991-2011 at the National Institutes of Health (NIH) and NIH Associate Director for Research on Women’s Health. Prior to that, she was Professor and Chair of the Department of Pathology at Howard University College of Medicine ...
Pinn, Vivian W., Bokka, Leshya, Patel, Anisha   +2 more
openaire   +1 more source

A PINN Framework for Perturbed Poromechanical Models

Solving partial differential equations with errors or performing regression with a small number of data alone may not yield accurate solutions. A Physics Informed Neural Network (PINN) model can offer a robust approach to address this issue. By incorporating both the governing physics and sensor data, PINNs can effectively address the discrepancies ...
Caterina Millevoi, Nicolò Spiezia, Massimiliano Ferronato   +2 more
openaire   +1 more source

GW-PINN: A Deep Learning Algorithm for Solving Groundwater Flow Equations

Advances in Water Resources, 2022
Xiaoping Zhang, Lili Ju, Ming Ye
exaly  

High-Dimensional Inverse Modeling of Hydraulic Tomography by Physics Informed Neural Network (HT-PINN)

Journal of Hydrology, 2023
Jian Luo, Chunhui LU, Quan Guo
exaly